TrackerDetector: A system to detect third-party trackers through machine learning

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• 作者：Qianru Wu ^{wuqianru11@mails.ucas.ac.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Qixu Liu ^{liuqixu@ucas.ac.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yuqing Zhang ; ^{zhangyq@ucas.ac.cn" class="auth_mail" title="E-mail the corresponding author} ; ^{fangzhejun09@mails.ucas.ac.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Guanxing Wen ^{wenguanxing10@mails.ucas.ac.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Web privacy ; Third-party tracking ; Classifier ; Blacklist ; BFTree
• 刊名：Computer Networks
• 出版年：2015
• 出版时间：14 November 2015
• 年：2015
• 卷：91
• 期：Complete
• 页码：164-173
• 全文大小：2471 K

文摘

Privacy violation caused by third-party tracking has become a serious problem, and the most effective defense against it is blocking. However, as the core part of blocking, the blacklist is usually manually curated and is difficult to maintain. To make it easier to generate a blacklist and reduce human work, we propose an effective system with high accuracy, named TrackerDetector, to detect third-party trackers automatically. Intuitively, the behaviors of trackers and non-trackers are different, which leads to different JavaScript API sets being called. Thus, an incremental classifier is trained from JavaScript files crawled from a large number of websites to detect whether a website is a third-party tracker. High accuracy of 97.34% is obtained with our dataset and that of 93.56% is obtained within a 10-fold cross validation.